GoBike Data Analysis¶

Goals¶

In this analysis, I aim to uncover any patterns, trends, or insights from a spreadsheet containing 519,000+ rows of data pertaining to bike rentals. The data contains basic information like:

Start & End times
Start & End locations & IDs
Bike IDs
User information - gender, birth year, customer type

Some Questions Worth Answering¶

Customer demographics
- What age group uses bikes more often?
- Which gender rents more bikes?
Ride statistics
- Average ride length?
- When were the most popular times to rent a bike?
Location demographics
- Do any stations see more traffic?
- What are the least used stations?
Miscellaneous
- Which bike was used the most/least?

Data Exploration¶

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 519700 entries, 0 to 519699
Data columns (total 15 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             519700 non-null  int64  
 1   start_time               519700 non-null  object 
 2   end_time                 519700 non-null  object 
 3   start_station_id         519700 non-null  int64  
 4   start_station_name       519700 non-null  object 
 5   start_station_latitude   519700 non-null  float64
 6   start_station_longitude  519700 non-null  float64
 7   end_station_id           519700 non-null  int64  
 8   end_station_name         519700 non-null  object 
 9   end_station_latitude     519700 non-null  float64
 10  end_station_longitude    519700 non-null  float64
 11  bike_id                  519700 non-null  int64  
 12  user_type                519700 non-null  object 
 13  member_birth_year        453159 non-null  float64
 14  member_gender            453238 non-null  object 
dtypes: float64(5), int64(4), object(6)
memory usage: 59.5+ MB

Observations¶

There's a lot of data we can take away from the above:

Duration: The average bike ride was ~1100 seconds, or just over 18 minutes
Birth Year: The average birth year is 1980, and 75% of customers were born before 1988
- Outliers: The oldest birth year is reported as 1886 - user error?
Gender: There are roughly ~3.5x as men male users as female/other users

Checking for Missing Data¶

As we can see below, two columns are missing ~12.8% of the data. For simplicity's sake, I removed these entries.

Customer Demographics¶

Single-Time Users vs. GoBike Subscribers¶

Age & Gender Distribution¶

Age vs. Ride Duration¶

Observations¶

Men between 25-44 are the main customers
Only ~7,500 people between 18-24 rent bikes - a bit surprising?
People between 30s-50s tend ride for 12-15 minutes - commuting to/from work?
Younger folk and seniors have more varied ride durations - less commitments, more free time, etc.

Date & Time¶

Earliest date: 2021-06-28 09:47:00
  Latest date: 2021-12-31 23:59:00

Label	Time
Early Morning	3:00 - 5:59am
Morning	6:00 - 11:59am
Afternoon	12:00pm - 4:59pm
Evening	5:00 - 8:59pm
Night	9:00 - 11:59pm
Late Night	12:00am - 2:59am

Observations¶

Fall seems to be the most popular time to rent bikes - temperatures cool down, but not too chilly yet
Mornings and evenings see the most rentals for each month - probably due to commute?
Very few night/late night rentals - weather & safety concerns?

Observations¶

Most of the traffic is in San Francisco - Market Street
- More rentals took place near BART stations, and the water - tourism?
Oakland saw significantly less rentals - maybe due to locals and less tourists?

Summary¶

People between 25-44 tend to rent bikes more often, but ride them for shorter durations.
People outside that age range tend to rent bikes less often, but ride them for longer durations.
Mornings and evenings see the most rentals, probably due to people's work commutes.
More bike rentals occur as summer becomes fall, but slowly drop as fall becomes winter.
The bike stations closer to major points of interest (BART stations, popular tourist areas) see more rentals.

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender
0	80110	12/31/2021 16:57	1/1/2022 15:12	74	Laguna St at Hayes St	37.776435	-122.426244	43	San Francisco Public Library (Grove St at Hyde...	37.778768	-122.415929	96	Customer	1987.0	Male
1	78800	12/31/2021 15:56	1/1/2022 13:49	284	Yerba Buena Center for the Arts (Howard St at ...	37.784872	-122.400876	96	Dolores St at 15th St	37.766210	-122.426614	88	Customer	1965.0	Female
2	45768	12/31/2021 22:45	1/1/2022 11:28	245	Downtown Berkeley BART	37.870348	-122.267764	245	Downtown Berkeley BART	37.870348	-122.267764	1094	Customer	NaN	NaN
3	62172	12/31/2021 17:31	1/1/2022 10:47	60	8th St at Ringold St	37.774520	-122.409449	5	Powell St BART Station (Market St at 5th St)	37.783899	-122.408445	2831	Customer	NaN	NaN
4	43603	12/31/2021 14:23	1/1/2022 2:29	239	Bancroft Way at Telegraph Ave	37.868813	-122.258764	247	Fulton St at Bancroft Way	37.867789	-122.265896	3167	Subscriber	1997.0	Female

	duration_sec	start_station_id	start_station_latitude	start_station_longitude	end_station_id	end_station_latitude	end_station_longitude	bike_id	member_birth_year
count	519700.000000	519700.000000	519700.000000	519700.000000	519700.000000	519700.000000	519700.000000	519700.000000	453159.000000
mean	1099.009521	95.034245	37.771653	-122.363927	92.184041	37.771844	-122.363236	1672.533079	1980.404787
std	3444.146451	86.083078	0.086305	0.105573	84.969491	0.086224	0.105122	971.356959	10.513488
min	61.000000	3.000000	37.317298	-122.444293	3.000000	37.317298	-122.444293	10.000000	1886.000000
25%	382.000000	24.000000	37.773492	-122.411726	23.000000	37.774520	-122.410345	787.000000	1974.000000
50%	596.000000	67.000000	37.783521	-122.398870	66.000000	37.783830	-122.398525	1728.500000	1983.000000
75%	938.000000	139.000000	37.795392	-122.391034	134.000000	37.795392	-122.391034	2520.000000	1988.000000
max	86369.000000	340.000000	37.880222	-121.874119	340.000000	37.880222	-121.874119	3733.000000	1999.000000

	column	%_missing	#_rows
0	member_birth_year	12.80	66541.0
1	member_gender	12.79	66462.0

	Count
member_gender
Female	98542
Male	348318
Other	6299